How One Startup Turned a $5,000 Contest Into Millions

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How One Startup Turned a $5,000 Contest Into Millions

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JetpacApp/Flickr

Last year, San Francisco startup Jetpac offered a $5,000 purse to anyone who could teach a computer to discern which of tens of thousands of vacation photos were any good. More than 200 contestants entered. The second-place winner's code now lives at the core of JetPac's iPad travel app, which sifts and displays your Facebook friends' best travel snapshots to inspire your next excursion.

On Thursday, the company announced that the results of the $5,000 contest had essentially netted them $2.4 million in venture capital funding, mainly from Khosla Ventures and Yahoo co-founder Jerry Yang. That's some math Jetpac doesn't need to outsource to appreciate.

Jetpac's fundraising represents a striking success for data science as sport�an approach that first drew attention when Netflix offered $1 million to whomever could best improve their movie recommendation algorithm. Ironically, Netflix never used the winning code. Not so at Jetpac.

"Before we had the algorithm, people would play with the prototype, and the experience was pretty painful," says Jetpac co-founder Pete Warden, who formerly worked at Apple as an image-processing engineer.

Jetpac ran its data contest through Kaggle, a platform for number-crunching competitions that has built some serious geek cachet during its short existence. NASA sponsored a competition to map the universe's dark matter. Facebook backed a nerd throwdown in which the prize was a job interview at Facebook. PayPal mafioso Max Levchin is chairman of the board.

Kaggle hosts public competitions, such as Jetpac's, in which anyone can try to solve the problem posed by the contest sponsor. Top competitors get invited to Kaggle's private contests, which often involve proprietary business data and bigger purses.

The beauty of solving data problems via contests is that like other sports, the final score speaks for itself. Math tells you which algorithm offers the most accurate results. It's a pure meritocracy.

Take Jetpac, which prior to the contest used humans to rate the quality of 30,000 shared photos. Contestants "trained" their algorithms using meta-data associated with 10,000 of the photos, then ran their code on the remaining 20,000 to see how well they'd done. The goal: Create an algorithm that could rank the quality of the photos in a way that most closely matched the subjective human ratings just by analyzing things like captions, photo size and location.

The algorithm Jetpac ultimately used came from the second-place finisher, whose code Warden says was nearly as accurate as the winner's but was much cleaner and easier to drop right into the company's own software. Caption words that indicated a bad vacation photo included "mommy," "graduation," "CEO" and "San Jose." The top word indicating a good photo was "tomb." (Warden says think European cathedrals and Angkor Wat.) The prize for second place: $1,500.

That amount pales next to $2.4 million, but Warden says the competitor, an Oxford physics Ph.D. who works days for a London-based investment fund doing "algorithmic trading," doesn't lose out entirely. Jetpac only gets a non-exclusive license to the algorithm, per Kaggle rules�though that shouldn't trouble the company's new backers, since Khosla also funds Kaggle.

"They did all the hard work and then allowed me and other Kagglers the opportunity to do the fun bit," Tigg says.

Warden says the Kaggle experience was so successful that Jetpac plans to sponsor more contests with its new money, including an effort to analyze the photos' visual data to see if an algorithm can judge a picture by "looking" at it. The contests give startups access to a level of smarts they otherwise couldn't afford, he says, since few true data geeks want to deal with the non-math�related headaches that come with startup life.

"They love the actual puzzle solving aspect of the machine learning," Warden says. "But they don't like all the other stuff that comes with a job."